Ibex Obtains CE Mark for Galen Gastric, the World's First AI-powered Solution for Gastric Cancer Detection

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Jun 15, 2022, 08:00 ET

Galen™ Gastric demonstrated outstanding outcomes in detecting gastric cancer in a multi-site study, supporting quality diagnosis, enhanced lab efficiency and quality control

TEL AVIV, Israel, June 15, 2022 /PRNewswire/ -- Ibex Medical Analytics, the pioneer in AI-powered cancer diagnostics, today announced CE mark for the Galen™ Gastric solution that supports pathologists in the detection of various types of gastric cancer. With this first-of-its-kind solution becoming generally available, Ibex offers the richest portfolio of artificial intelligence (AI) solutions in pathology, enabling detection of cancer and other pathologies in gastric, breast and prostate biopsies.

Gastric cancer is a prominent malignant disease in both men and women worldwide, with over a million new cases every year and a relatively poor prognosis. Pathologists play a crucial role in the detection and diagnosis of gastric cancer, with their assessments being vital for reaching correct treatment decisions by oncologists and improving patient survival rates.

Over the last several years there has been an increase in overall cancer incidence, and rapid advances in personalized medicine have resulted in increases in the complexity of cancer diagnosis. Coupled with a global decline in the number of pathologists, these trends have led to growing workloads imposed on pathology departments. Clearly, there is a growing need for automated solutions and decision-support tools that help pathologists detect cancer to the utmost accuracy more rapidly, while enabling comprehensive and affordable quality control.

The Galen suite of solutions is the most widely deployed AI technology in pathology, and Ibex now partners with laboratories, hospitals and health systems to implement AI for the diagnosis of gastric biopsies. Galen Gastric is an integrated diagnostics solution that supports pathologists in the detection of gastric cancer, H. pylori and other important clinical findings and in enabling shorter turnaround times and optimized diagnostic workflows¹. The solution was developed by a team of pathologists, data scientists and software engineers who implemented advanced Deep Learning technologies and trained algorithms on more than a million image samples, scanned from biopsy slides digitized using digital pathology.

The CE mark follows pioneering results from a blinded, multi-site clinical study at Medipath, the largest pathology network in France, and at Maccabi Healthcare Services, the second largest health network in Israel. Galen Gastric demonstrated very high accuracy in detecting various types of gastric cancer, as well as Helicobacter pylori (H. pylori - a common gastric bacterial infection and a precursor for cancer), neuroendocrine lesions, dysplasia, adenoma and additional pathologies. These results validate the robustness of Galen Gastric and support its adoption by pathology institutes that aim to improve diagnostic quality and enhance productivity.

"Diagnosis of gastric biopsies remains a challenging task for pathologists. Gastric cancers are relatively scarce, often minute and sometimes easy to miss," said Judith Sandbank, MD, Director of the Pathology Institute at Maccabi Healthcare Services², and the principal investigator in the study. "We were impressed with the successful outcomes and the very high accuracy demonstrated by Galen Gastric in detection of different cancer types. It's also encouraging to see an AI solution that goes beyond cancer detection to accurately identify H. pylori, and provide additional insights on dysplasia, lymphoma, gastritis and other pathologies. We have already started to use Galen Gastric in Maccabi and it helped us improve the quality of cancer diagnosis and patient care. I am looking forward to seeing this technology now becoming widely available at pathology departments around the world."

"We are proud to obtain this first-of-its-kind CE mark for AI-powered GI diagnostics in pathology, following excellent performance in a multi-site clinical study," said Chaim Linhart, PhD, Co-founder and CTO at Ibex Medical Analytics. "We trained the Deep Learning models of Galen Gastric on more than a million image samples from multiple labs to ensure it can accurately detect not only cancer but also a multitude of other clinically relevant features that impact future treatment for patients. Ibex now offers an unprecedented breadth of clinical applications, and this approval will enable our growing customer base to expand the usage of AI technology and provide pathologists with new insights as they review gastric biopsies, improving the quality of cancer diagnosis and care."

The study outcomes will be presented at the European Congress on Digital Pathology which takes place in Berlin between June 15-18 (Ibex presents at booth B11).

About Ibex Medical Analytics

Ibex pioneers AI-powered cancer diagnostics in pathology. We empower physicians to provide every patient with an accurate, timely and personalized cancer diagnosis by developing clinical-grade AI algorithms and digital workflows that help detect and grade cancer in biopsies. Our Galen™ platform is the first-ever AI-powered integrated diagnostics solution in pathology and used in routine clinical practice worldwide, supporting pathologists and providers in improving the quality and accuracy of diagnosis, implementing comprehensive quality control, reducing turnaround times and boosting productivity with more efficient workflows. Ibex's Artificial Intelligence technology is built on Deep Learning algorithms trained by a team of pathologists, data scientists and software engineers.

For more information, go to www.ibex-ai.com.

Media contact:
Chaya Zabihi
GK for Ibex
[email protected]

[1] Sandbank et al. Validation and Clinical Deployment of an AI-Based Solution for Detection of Gastric Adenocarcinoma and Helicobacter pylori in Gastric Biopsies. USCAP 2022.

[2] Dr. Sandbank also serves as the Chief Medical Officer at Ibex Medical Analytics

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SOURCE Ibex Medical Analytics